Idea Discovery Robot
Workflow 1 adaptation for robotics and embodied AI.
Install
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill idea-discovery-robot -g -a claude-code -y- Repository
- wanshuiyin/Auto-claude-code-research-in-sleep18.4k
- Category
- Engineering
- Updated
- 4d ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Workflow 1 adaptation for robotics and embodied AI. Orchestrates robotics-aware literature survey, idea generation, novelty check, and critical review to go from a broad robotics direction to benchmark-grounded, simulation-first ideas. Use when user says \"robotics idea discovery\", \"机器人找idea\", \"embodied AI idea\", \"机器人方向探索\", \"sim2real 选题\", or wants ideas for manipulation, locomotion, navigation, drones, humanoids, or general robot learning.
Robotics Idea Discovery Pipeline
Before each reviewer slot, follow Selectable Reviewers: resolve invocation →
.aris/reviewer.json→ Claude Code default (Codex MCP) withreviewer_route.pythrough the canonical helper resolver, preserving this slot's tier, fresh contexts, source/image inputs and deterministic gates. Codex examples/pins below apply only tobackend=codex; other routes use their own first-call/reply contract. Save the route and actual returned identity; unavailable selections areREVIEW_UNAVAILABLE. Classify acceptance from actual families/completion/scope. Forward invocation reviewer/model/effort overrides to reviewer-bearing children. Generation workers are unchanged.
Orchestrate a robotics-specific idea discovery workflow for: $ARGUMENTS
Overview
This skill chains four sub-skills into a single automated pipeline:
/research-lit → /idea-creator (robotics framing) → /novelty-check → /research-review
(survey) (filter + pilot plan) (verify novel) (critical feedback)
But every phase must be grounded in robotics-specific constraints:
- Embodiment: arm, mobile manipulator, drone, humanoid, quadruped, autonomous car, etc.
- Task family: grasping, insertion, locomotion, navigation, manipulation, rearrangement, multi-step planning
- Observation + action interface: RGB/RGB-D/tactile/language; torque/velocity/waypoints/end-effector actions
- Simulator / benchmark availability: simulation-first by default
- Real robot constraints: hardware availability, reset cost, safety, operator time
- Evaluation quality: success rate plus failure cases, safety violations, intervention count, latency, sample efficiency
- Sim2real story: whether the idea can stay in sim, needs offline logs, or truly requires hardware
